In the new era of big data, companies and other organizations have access to vast amounts of structured and unstructured data as well as access to a variety of new data sources. As a result, many data analytics applications have been developed to provide users with insight into their data. One example genre of data analytics applications includes workforce analytics. Workforce analytics applications are used by businesses and other organizations to assist users in understanding their data, making appropriate decisions, and find answers to key questions to gain the insight needed to take actions. Workforce analytics applications are adapted for providing statistical models to worker-related data, allowing companies to optimize their various enterprise processes.
A company's data may change on a continuous basis. For example, employee records may be updated, added, or deleted. When this happens, the data stored in the database needs to be reprocessed. In one example, a service provider may maintain the data for multiple companies and update data on a recurring basis, such as hourly, or daily. For example, each company's updates are processed during the night. In some cases, some company's updates may take longer than other companies' updates. If the companies with longer updates are always processed first, then other companies' updates may not be fairly processed.